Install & use

Requires JupyterLab ≥ 4.2 and at least one ACP agent on your PATH — e.g. claude-agent-acp (Claude Code) or opencode — or any agent from the ACP registry, which runs on demand via npx/uvx/downloaded binary.

pip install jupyterlab-acp

The wheel ships the prebuilt labextension, so that’s all you need — launch jupyter lab and you’re set.

From source (development)¶

JupyterLab is needed at build time to compile the extension.

git clone https://github.com/SchmidtDSE/jupyterlab-acp
cd jupyterlab-acp
pip install jupyterlab hatchling hatch-jupyter-builder editables   # build tooling (jlpm)
jlpm install && jlpm build
pip install -e . --no-build-isolation                              # hook reuses the built JS
jupyter labextension develop --overwrite .

Use¶

jupyter lab

• Click the chat icon in the left sidebar for the docked assistant, or New ACP Chat in the launcher to open a chat as a main-area tab (open as many as you like; drag/split them, file browser stays docked).

• Pick a Configured agent, or browse the ACP Registry cards and click one to launch it on demand.

• The model / mode selectors below the input reflect what the agent advertises; type / for its slash commands.

• When the agent wants a tool, an approval card appears — allow or reject. Approve a notebook edit and watch the open .ipynb update live.

Configuring agents¶

Built-in harnesses can be extended or overridden from jupyter_server_config.py:

c.AcpExtension.harnesses = [
    {"id": "my-agent", "display_name": "My Agent",
     "command": "my-agent", "args": ["--acp"], "env": {"API_KEY": "..."}},
]

Develop¶

python -m pytest        # 47 tests: ACP core, capabilities, binding, handlers
                        # (real-server), serializer, permission, registry
jlpm build:lib          # typecheck the frontend